if(!require("plotly")) {install.packages("plotly")}
Loading required package: plotly
Loading required package: ggplot2
Find out what's changed in ggplot2 at https://github.com/tidyverse/ggplot2/releases.
Attaching package: ‘plotly’
The following object is masked from ‘package:ggplot2’:
last_plot
The following object is masked from ‘package:stats’:
filter
The following object is masked from ‘package:graphics’:
layout
# install.packages("latex2exp")
# install.packages("BiocManager")
# install.packages("corrplot")
# BiocManager::install("EBImage")
if(!require("lme4")){install.packages("lme4")}
Loading required package: lme4
Loading required package: Matrix
if(!require("lmerTest")){install.packages("lmerTest")}
Loading required package: lmerTest
Attaching package: ‘lmerTest’
The following object is masked from ‘package:lme4’:
lmer
The following object is masked from ‘package:stats’:
step
if(!require("nlme")){install.packages("nlme")}
Loading required package: nlme
Attaching package: ‘nlme’
The following object is masked from ‘package:lme4’:
lmList
if(!require("formattable")){install.packages("formattable")}
Loading required package: formattable
Attaching package: ‘formattable’
The following object is masked from ‘package:plotly’:
style
if(!require("xgboost")){install.packages("xgboost")}
Loading required package: xgboost
Attaching package: ‘xgboost’
The following object is masked from ‘package:plotly’:
slice
if(!require("processx")) {install.packages("processx")}
Loading required package: processx
if(!require("mefa")){install.packages("mefa")}
Loading required package: mefa
mefa 3.2-7 2016-01-11
library(plotly)
library(lme4)
library(lmerTest)
library(nlme)
library(formattable)
library(xgboost)
### Load libraries
library(EBImage)
Attaching package: ‘EBImage’
The following object is masked from ‘package:plotly’:
toRGB
library(ggplot2)
library(stringr)
library(gridExtra)
Attaching package: ‘gridExtra’
The following object is masked from ‘package:EBImage’:
combine
library(latex2exp)
Attaching package: ‘latex2exp’
The following object is masked from ‘package:plotly’:
TeX
packageVersion('plotly')
[1] ‘4.9.1’
Sys.setenv("plotly_username"="thuynh32")
Sys.setenv("plotly_api_key"="xcSv1yzujDc1IGEwQlr2")
drive1 <- read.csv('../../../data/TT1/preprocessed/Analysis/TT1_Drive_1_PP.csv')
drive2 <- read.csv('../../../data/TT1/preprocessed/Analysis/TT1_Drive_2_PP.csv')
drive3 <- read.csv('../../../data/TT1/preprocessed/Analysis/TT1_Drive_3_PP.csv')
drive4 <- read.csv('../../../data/TT1/preprocessed/Analysis/TT1_Drive_4_PP.csv', stringsAsFactors = T)
combinedDf <- cbind(drive4,
drive2$MeanPP_SegMax, drive3$MeanPP_SegMax,
drive1$MeanPP_Seg0, drive2$MeanPP_Seg0, drive3$MeanPP_Seg0,
drive2$StdPP, drive3$StdPP
)
names(combinedDf) <- c(names(drive4), "PP_Dev_2_Straight", "PP_Dev_3_Straight", "PP_Dev_1_Turning", "PP_Dev_2_Turning", "PP_Dev_3_Turning", "Std_PP_2", "Std_PP_3")
combinedDf$Subject <- paste0("#", str_pad(combinedDf$Subject, 2, pad="0"))
combinedDf_NoStressor <- combinedDf[combinedDf$Activity == "NO",]
combinedDf_Cognitive <- combinedDf[combinedDf$Activity == "C",]
combinedDf_Motoric <- combinedDf[combinedDf$Activity == "M",]
combinedDf_NoStressor$Subject <- as.factor(combinedDf_NoStressor$Subject)
combinedDf_Cognitive$Subject <- as.factor(combinedDf_Cognitive$Subject)
combinedDf_Motoric$Subject <- as.factor(combinedDf_Motoric$Subject)
COLOR_NORMAL <- list(color='rgb(120,120,120)')
COLOR_COGNITIVE <- list(color='rgb(158,202,225)')
COLOR_MOTORIC <- list(color='rgb(58,200,225)')
COLOR_FAILURE <- list(color='red')
yAxis <- list(
title = 'Perinasal Perspiration (Log)',
range=c(-0.3, 0.5)
)
THRESHOLD_MILD = 0.07
THRESHOLD_EXTREME = 0.2
MARKER_LINE_MILD = list(color="blue")
MARKER_LINE_EXTREME = list(color="red")
fig_NoStressor <- plot_ly(combinedDf_NoStressor, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>%
add_segments(x="#01", xend="#41", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
line=list(color="blue", dash = 'dot')) %>%
# add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
# line=list(color="darkred", dash = 'dot')) %>%
layout(yaxis = yAxis, barmode = 'group', title="Failure Driving \n Group=No Stressor")
htmltools::tagList(fig_NoStressor)
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
fig_Cognitive <- plot_ly(combinedDf_Cognitive, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>%
add_segments(x="#02", xend="#22", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
line=list(color="blue", dash = 'dot')) %>%
# add_segments(x="#02", xend="#22", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
# line=list(color="darkred", dash = 'dot')) %>%
layout(yaxis = yAxis, barmode = 'group', title="Failure Driving \n Group=Cognitive")
htmltools::tagList(fig_Cognitive)
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
fig_Motoric <- plot_ly(combinedDf_Motoric, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>%
add_segments(x="#05", xend="#31", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
line=list(color="blue", dash = 'dot')) %>%
# add_segments(x="#05", xend="#31", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
# line=list(color="darkred", dash = 'dot')) %>%
layout(yaxis = yAxis, barmode = 'group', title="Failure Driving \n Group: Motoric")
htmltools::tagList(fig_Motoric)
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
library(nlme)
combinedDf$Subject = as.factor(combinedDf$Subject)
combinedDf$Activity = as.factor(combinedDf$Activity)
combinedDf$PP_Dev_Group = ifelse(combinedDf$PP_Dev > THRESHOLD_MILD, 1, 0)
model = lme(PP_Dev ~
abs(PP_Dev_2_Straight)
+ abs(PP_Dev_3_Straight)
+ abs(PP_Dev_2_Turning)
+ abs(PP_Dev_3_Turning)
+ factor(Activity),
random=~1|Subject,
data=combinedDf,
method="REML")
# anova(model)
summary(model)
Linear mixed-effects model fit by REML
Data: combinedDf
Random effects:
Formula: ~1 | Subject
(Intercept) Residual
StdDev: 0.07501618 0.02813106
Fixed effects: PP_Dev ~ abs(PP_Dev_2_Straight) + abs(PP_Dev_3_Straight) + abs(PP_Dev_2_Turning) + abs(PP_Dev_3_Turning) + factor(Activity)
Correlation:
(Intr) a(PP_D_2_S a(PP_D_3_S a(PP_D_2_T a(PP_D_3_T fc(A)M
abs(PP_Dev_2_Straight) 0.598
abs(PP_Dev_3_Straight) 0.082 0.028
abs(PP_Dev_2_Turning) -0.508 -0.715 -0.483
abs(PP_Dev_3_Turning) -0.586 -0.456 -0.706 0.529
factor(Activity)M -0.385 -0.171 0.355 -0.181 -0.125
factor(Activity)NO -0.154 0.026 0.547 -0.450 -0.368 0.616
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-0.440076168 -0.154672364 -0.002413178 0.107752315 0.674410551
Number of Observations: 21
Number of Groups: 21
plot(model)

model = lme(PP_Dev ~
abs(PP_Dev_2_Turning)
+ factor(Activity),
random=~1|Subject,
data=combinedDf,
method="REML")
# anova(model)
summary(model)
Linear mixed-effects model fit by REML
Data: combinedDf
Random effects:
Formula: ~1 | Subject
(Intercept) Residual
StdDev: 0.08150667 0.030565
Fixed effects: PP_Dev ~ abs(PP_Dev_2_Turning) + factor(Activity)
Correlation:
(Intr) a(PP_D fc(A)M
abs(PP_Dev_2_Turning) -0.497
factor(Activity)M -0.457 -0.270
factor(Activity)NO -0.346 -0.423 0.550
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-0.49096784 -0.21744976 0.01851302 0.20552883 0.50745083
Number of Observations: 21
Number of Groups: 21
plot(model)

model = lme(PP_Dev ~
PP_Dev_2_Straight +
PP_Dev_3_Straight +
PP_Dev_1_Turning +
PP_Dev_2_Turning +
PP_Dev_3_Turning +
Std_PP_2 +
Std_PP_3 +
factor(Activity),
random=~1|Subject,
data=combinedDf,
method="REML")
# anova(model)
summary(model)
Linear mixed-effects model fit by REML
Data: combinedDf
Random effects:
Formula: ~1 | Subject
(Intercept) Residual
StdDev: 0.08634381 0.03237893
Fixed effects: PP_Dev ~ PP_Dev_2_Straight + PP_Dev_3_Straight + PP_Dev_1_Turning + PP_Dev_2_Turning + PP_Dev_3_Turning + Std_PP_2 + Std_PP_3 + factor(Activity)
Correlation:
(Intr) PP_D_2_S PP_D_3_S PP_D_1 PP_D_2_T PP_D_3_T S_PP_2 S_PP_3 fc(A)M
PP_Dev_2_Straight -0.056
PP_Dev_3_Straight 0.048 -0.363
PP_Dev_1_Turning -0.797 0.130 0.176
PP_Dev_2_Turning 0.360 -0.754 0.023 -0.422
PP_Dev_3_Turning -0.140 0.268 -0.700 -0.283 -0.252
Std_PP_2 0.199 -0.570 -0.066 -0.333 0.816 -0.134
Std_PP_3 -0.818 0.423 -0.201 0.672 -0.688 0.275 -0.604
factor(Activity)M 0.162 -0.317 0.495 -0.054 0.166 -0.372 0.128 -0.430
factor(Activity)NO -0.228 -0.225 0.426 0.191 -0.123 -0.235 -0.263 0.124 0.481
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-0.48616985 -0.10946874 -0.02066106 0.16055277 0.56942657
Number of Observations: 21
Number of Groups: 21
plot(model)

combinedDf$PP_Dev <- NULL
combinedDf$Subject <- NULL
combinedDf$Activity_NO <- ifelse(combinedDf$Activity == "NO", 1, 0)
combinedDf$Activity_C <- ifelse(combinedDf$Activity == "C", 1, 0)
combinedDf$Activity_M <- ifelse(combinedDf$Activity == "M", 1, 0)
combinedDf$Activity <- NULL
combinedDf$Class <- ifelse(combinedDf$PP_Dev_Group == 1, T, F)
combinedDf$PP_Dev_Group <- NULL
# library(mefa)
# combinedDf <- rep(combinedDf, 10)
# s=55, f=4
set.seed(5151)
n_folds <- 3
params <- param <- list(objective = "binary:logistic",
booster = "gbtree",
eval_metric = "auc",
eta = 0.1,
max_depth = 8,
alpha = 1,
lambda = 0,
gamma = 0.3,
min_child_weight = 0.3,
subsample = 1,
colsample_bytree = 0.5)
# xgb_m <- xgb.cv( params = param,
# data = as.matrix(combinedDf %>% select(-Class)) ,
# label = combinedDf$Class,
# nrounds = 100,
# verbose = F,
# prediction = T,
# maximize = T,
# nfold = n_folds,
# metrics = "auc",
# early_stopping_rounds = 100,
# stratified = F,
# scale_pos_weight = 0.5)
#
# # xgb_m$evaluation_log[xgb_m$best_iteration,"test_auc_mean"]
# xgb_m$evaluation_log[xgb_m$best_iteration,]
# Error
xgb_m <- xgb.cv( params = param,
data = as.matrix(combinedDf %>% select(-Class)) ,
label = combinedDf$Class,
nrounds = 500,
verbose = F,
prediction = T,
maximize = T,
nfold = n_folds,
metrics = c("auc", "error"),
early_stopping_rounds = 100,
stratified = F,
scale_pos_weight = 3.05)
# xgb_m$evaluation_log[xgb_m$best_iteration,"test_auc_mean"]
xgb_m$evaluation_log[xgb_m$best_iteration,]
NA
library(pROC)
# it = which.max(xgb_m$evaluation_log$test_auc_mean)
# best.iter = xgb_m$evaluation_log$iter[it]
# best.iter
plot(pROC::roc(response = ifelse(combinedDf$Class==T, 1, 0),
predictor = xgb_m$pred,
levels=c(0, 1)),
lwd=1.5)
Setting direction: controls < cases

---
title: "R Notebook"
output: html_notebook
---

```{r}
if(!require("plotly")) {install.packages("plotly")}

# install.packages("latex2exp")
# install.packages("BiocManager") 
# install.packages("corrplot")
# BiocManager::install("EBImage")

if(!require("lme4")){install.packages("lme4")}
if(!require("lmerTest")){install.packages("lmerTest")}
if(!require("nlme")){install.packages("nlme")}
if(!require("formattable")){install.packages("formattable")}
if(!require("xgboost")){install.packages("xgboost")}
if(!require("processx")) {install.packages("processx")}

if(!require("mefa")){install.packages("mefa")}

library(plotly)
library(lme4)
library(lmerTest)
library(nlme)
library(formattable)
library(xgboost)

### Load libraries
library(EBImage)
library(ggplot2)
library(stringr)
library(gridExtra)
library(latex2exp)
packageVersion('plotly')
Sys.setenv("plotly_username"="thuynh32")
Sys.setenv("plotly_api_key"="xcSv1yzujDc1IGEwQlr2")

```

```{r}
drive1 <- read.csv('../../../data/TT1/preprocessed/Analysis/TT1_Drive_1_PP.csv')
drive2 <- read.csv('../../../data/TT1/preprocessed/Analysis/TT1_Drive_2_PP.csv')
drive3 <- read.csv('../../../data/TT1/preprocessed/Analysis/TT1_Drive_3_PP.csv')
drive4 <- read.csv('../../../data/TT1/preprocessed/Analysis/TT1_Drive_4_PP.csv', stringsAsFactors = T)
```

```{r}
combinedDf <- cbind(drive4, 
                    drive1$MeanPP_Seg0, 
                    drive2$MeanPP_SegMax, drive3$MeanPP_SegMax, 
                    drive2$MeanPP_Seg0, drive3$MeanPP_Seg0,
                    drive2$StdPP, drive3$StdPP 
                  )
names(combinedDf) <- c(names(drive4), 
                       "PP_Dev_1_Turning",
                       "PP_Dev_2_Straight", "PP_Dev_3_Straight", 
                       "PP_Dev_2_Turning", "PP_Dev_3_Turning", 
                       "Std_PP_2", "Std_PP_3")

combinedDf$Subject <- paste0("#", str_pad(combinedDf$Subject, 2, pad="0"))
```

```{r}
combinedDf_NoStressor <- combinedDf[combinedDf$Activity == "NO",]
combinedDf_Cognitive <- combinedDf[combinedDf$Activity == "C",]
combinedDf_Motoric <- combinedDf[combinedDf$Activity == "M",]

combinedDf_NoStressor$Subject <- as.factor(combinedDf_NoStressor$Subject)
combinedDf_Cognitive$Subject <- as.factor(combinedDf_Cognitive$Subject)
combinedDf_Motoric$Subject <- as.factor(combinedDf_Motoric$Subject)
```

```{r}
COLOR_NORMAL <- list(color='rgb(120,120,120)')
COLOR_COGNITIVE <- list(color='rgb(158,202,225)')
COLOR_MOTORIC <- list(color='rgb(58,200,225)')
COLOR_FAILURE <- list(color='red')

yAxis <- list(
  title = 'Perinasal Perspiration (Log)',
  range=c(-0.3, 0.5)
)

THRESHOLD_MILD = 0.07
THRESHOLD_EXTREME = 0.2

MARKER_LINE_MILD = list(color="blue")
MARKER_LINE_EXTREME = list(color="red")
```

```{r, warning=F}
fig_NoStressor <- plot_ly(combinedDf_NoStressor, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
  add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>% 
  add_segments(x="#01", xend="#41", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
                           line=list(color="blue", dash = 'dot')) %>%
  # add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
  #                          line=list(color="darkred", dash = 'dot')) %>%
  layout(yaxis = yAxis, barmode = 'group', title="Failure Driving \n Group=No Stressor")

htmltools::tagList(fig_NoStressor)
```

```{r, warning=F}
fig_Cognitive <- plot_ly(combinedDf_Cognitive, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
  add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>% 
  add_segments(x="#02", xend="#22", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
                           line=list(color="blue", dash = 'dot')) %>%
  # add_segments(x="#02", xend="#22", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
  #                          line=list(color="darkred", dash = 'dot')) %>%
  layout(yaxis = yAxis, barmode = 'group', title="Failure Driving \n Group=Cognitive")

htmltools::tagList(fig_Cognitive)
```



```{r, warning=F}
fig_Motoric <- plot_ly(combinedDf_Motoric, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
  add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>% 
  add_segments(x="#05", xend="#31", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
                           line=list(color="blue", dash = 'dot')) %>%
  # add_segments(x="#05", xend="#31", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
  #                          line=list(color="darkred", dash = 'dot')) %>%
  layout(yaxis = yAxis, barmode = 'group', title="Failure Driving \n Group: Motoric")

htmltools::tagList(fig_Motoric)
```


```{r}
library(nlme)

combinedDf$Subject = as.factor(combinedDf$Subject)
combinedDf$Activity = as.factor(combinedDf$Activity)

combinedDf$PP_Dev_Group = ifelse(combinedDf$PP_Dev > THRESHOLD_MILD, 1, 0)
```

```{r}
model = lme(PP_Dev ~ 
              abs(PP_Dev_2_Straight)
              + abs(PP_Dev_3_Straight)
              + abs(PP_Dev_2_Turning) 
              + abs(PP_Dev_3_Turning)
              + factor(Activity), 
            random=~1|Subject,
            data=combinedDf,
            method="REML")

# anova(model)
summary(model)
plot(model)
```

```{r}
model = lme(PP_Dev ~ 
              abs(PP_Dev_2_Turning)
              + factor(Activity), 
            random=~1|Subject,
            data=combinedDf,
            method="REML")

# anova(model)
summary(model)
plot(model)
```

```{r}
model = lme(PP_Dev ~ 
              PP_Dev_2_Straight + 
              PP_Dev_3_Straight + 
              PP_Dev_1_Turning + 
              PP_Dev_2_Turning + 
              PP_Dev_3_Turning + 
              Std_PP_2 + 
              Std_PP_3 +
              factor(Activity), 
            random=~1|Subject,
            data=combinedDf,
            method="REML")

# anova(model)
summary(model)
plot(model)
```

```{r}
combinedDf$PP_Dev <- NULL

combinedDf$Subject <- NULL
combinedDf$Activity_NO <- ifelse(combinedDf$Activity == "NO", 1, 0)
combinedDf$Activity_C <- ifelse(combinedDf$Activity == "C", 1, 0)
combinedDf$Activity_M <- ifelse(combinedDf$Activity == "M", 1, 0)
combinedDf$Activity <- NULL

combinedDf$Class <- ifelse(combinedDf$PP_Dev_Group == 1, T, F)
combinedDf$PP_Dev_Group <- NULL
```

```{r}
# library(mefa)
# combinedDf <- rep(combinedDf, 10) 
```

```{r}
# s=55, f=4

set.seed(5151) 
n_folds <- 3
params <- param <- list(objective       = "binary:logistic", 
               booster          = "gbtree",
               eval_metric      = "auc",
               eta              = 0.1,
               max_depth        = 8,
               alpha            = 1,
               lambda           = 0,
               gamma            = 0.3,
               min_child_weight = 0.3,
               subsample        = 1,
               colsample_bytree = 0.5)
           
# xgb_m <- xgb.cv(   params               = param,
#                   data = as.matrix(combinedDf %>% select(-Class)) ,
#                   label =  combinedDf$Class,
#                   nrounds             = 100,
#                   verbose             = F,
#                   prediction          = T,
#                   maximize            = T,
#                   nfold = n_folds,
#                   metrics  = "auc",
#                   early_stopping_rounds = 100,
#                   stratified       = F,
#                   scale_pos_weight = 0.5)
# 
# # xgb_m$evaluation_log[xgb_m$best_iteration,"test_auc_mean"]
# xgb_m$evaluation_log[xgb_m$best_iteration,]

# Error         
xgb_m <- xgb.cv(   params               = param,
                  data = as.matrix(combinedDf %>% select(-Class)) ,
                  label =  combinedDf$Class,
                  nrounds             = 500,
                  verbose             = F,
                  prediction          = T,
                  maximize            = T,
                  nfold               = n_folds,
                  metrics             = c("auc", "error"),
                  early_stopping_rounds = 100,
                  stratified            = F,
                  scale_pos_weight      = 3.05)

# xgb_m$evaluation_log[xgb_m$best_iteration,"test_auc_mean"]
xgb_m$evaluation_log[xgb_m$best_iteration,]

```

```{r}
library(pROC)

# it = which.max(xgb_m$evaluation_log$test_auc_mean)
# best.iter = xgb_m$evaluation_log$iter[it]
# best.iter 

plot(pROC::roc(response = ifelse(combinedDf$Class==T, 1, 0),
               predictor = xgb_m$pred,
               levels=c(0, 1)),
     lwd=1.5) 
```



